{{{;}#q8?\. Liang, P., Jordan, Michael, I., Klein, D. Scaling up abstraction refinement via pruning. How Much is 131 Million Dollars? ZFN-edited cells maintained both pluripotency and long-term reporter gene expression. Certified Defenses for Data Poisoning Attacks. They are now the foundation of today's NLP systems. Pierson, E., Koh, P. W., Hashimoto, T., Koller, D., Leskovec, J., Eriksson, N., Liang, P. Kulal, S., Pasupat, P., Chandra, K., Lee, M., Padon, O., Aiken, A., Liang, P., Wallach, H., Larochelle, H., Beygelzimer, A., d'Alche-Buc, F., Fox, E., Garnett, R. NEURAL INFORMATION PROCESSING SYSTEMS (NIPS). The following articles are merged in Scholar. Guu, K., Pasupat, P., Liu, E., Liang, P., Barzilay, R., Kan, M. Y. Kumar, A., Ma, T., Liang, P., Daume, H., Singh, A. His two research goals are (i) to make machine learning more robust, fair, and interpretable; and (ii) to make computers easier to communicate with through natural language. % Khani, F., Rinard, M., Liang, P., Erk, K., Smith, N. A. Wager, S., Fithian, W., Liang, P., Hazan, T., Papandreou, G., Tarlow, D. Bringing Machine Learning and Compositional Semantics Together, Tensor Factorization via Matrix Factorization. Try again later. A dynamic evaluation of static heap abstractions. Want to learn about meta-learning & few-shot learning? View details for DOI 10.1097/FJC.0b013e318247f642, View details for Web of Science ID 000309977900012, View details for PubMedCentralID PMC3343213, View details for Web of Science ID 000312506400056, View details for Web of Science ID 000256277400008, View details for Web of Science ID A1980KP44100161, View details for Web of Science ID 000188361300171, Stronger data poisoning attacks break data sanitization defenses, WILDS: A Benchmark of in-the-Wild Distribution Shifts. Their, This "Cited by" count includes citations to the following articles in Scholar. The sapogenins obtained from chlorogalum pomeridianum, Freeman Spogli Institute for International Studies, Institute for Computational and Mathematical Engineering (ICME), Institute for Human-Centered Artificial Intelligence (HAI), Institute for Stem Cell Biology and Regenerative Medicine, Stanford Institute for Economic Policy Research (SIEPR), Stanford Woods Institute for the Environment, Office of VP for University Human Resources, Office of Vice President for Business Affairs and Chief Financial Officer, Artificial Intelligence: Principles and Techniques, Writing Intensive Senior Research Project, Understanding and Developing Large Language Models, DOI 10.1146/annurev-linguist-030514-125312. Percy Liang: Stanford University Professor, technologist, and researcher in AI 7,897 views Mar 25, 2020 Stanford University Professor Percy Liang discusses the challenges of. 390Jane Stanford Way Shi, T., Steinhardt, J., Liang, P., Lebanon, G., Vishwanathan, S. V. Environment-Driven Lexicon Induction for High-Level Instructions. "t a","H Two students from his lab quit during their term because of his constant verbal abuse and harassment. "FV %H"Hr ![EE1PL* rP+PPT/j5&uVhWt :G+MvY c0 L& 9cX& When Percy Liang isn't creating algorithms, he's creating musical rhythms. High efficiency of ZFN-mediated targeted integration was achieved in both human embryonic stem cells and induced pluripotent stem cells. Khani, F., Liang, P., Daume, H., Singh, A. Conversations are often depressing and toxic. On the UK Biobank human health dataset, our model reconstructs the observed data while learning interpretable rates of aging associated with diseases, mortality, and aging risk factors. I also consult part-time for Open Philanthropy. Get ready to read Amazing lectures Clear grading criteria. On the interaction between norm and dimensionality: multiple regimes in learning. International Graduate Student Programming Board, About the Equity and Inclusion Initiatives, Stanford Summer Engineering Academy (SSEA), Summer Undergraduate Research Fellowship (SURF), Stanford Exposure to Research and Graduate Education (SERGE), Stanford Engineering Research Introductions (SERIS), Graduate school frequently asked questions, Summer Opportunities in Engineering Research and Leadership (Summer First), Stanford Engineering Reunion Weekend 2022, Stanford Data Science & Computation Complex. Sharma, R., Gupta, S., Hariharan, B., Aiken, A., Liang, P., Nori, Aditya, V. Spectral experts for estimating mixtures of linear regressions. His awards include the Presidential Early Career Award for Scientists and Engineers (2019), IJCAI Computers and Thought Award (2016), an NSF CAREER Award (2016), a Sloan Research Fellowship (2015), and a Microsoft Research Faculty Fellowship (2014). roughly $320,000 to $350,000 per year). Koh, P., Ang, K., Teo, H. K., Liang, P., Wallach, H., Larochelle, H., Beygelzimer, A., d'Alche-Buc, F., Fox, E., Garnett, R. Kumar, A., Liang, P., Ma, T., Wallach, H., Larochelle, H., Beygelzimer, A., d'Alche-Buc, F., Fox, E., Garnett, R. Unlabeled Data Improves Adversarial Robustness. Percy Liang is an Associate Professor of Computer Science at Stanford University (B.S. FAQs specific to the Honors Cooperative Program. Lots of homework Accessible outside class Group projects. Best professor in Tepper. /Length 11 0 R Learning dependency-based compositional semantics. Percy Liang is an Associate Professor of Computer Science and Statistics at Stanford University. /N 3 Programming languages & software engineering. Haghighi, A., Liang, P., Berg-Kirkpatrick, T., Klein, D. Structure compilation: trading structure for features. We present a probabilistic model of diachronic phonology in which individual word forms undergo stochastic edits along the branches of a phylogenetic tree. Training accurate classifiers requires many labels, but each label provides only limited information (one bit for binary classification). xwXSsN`$!l{@ $@TR)XZ( RZD|y L0V@(#q `= nnWXX0+; R1{Ol (Lx\/V'LKP0RX~@9k(8u?yBOr y Liang, P., Jordan, Michael, I., Taskar, B. Liang, P., Bach, F., Bouchard, G., Jordan, Michael, I. Optimal team size and monitoring in organizations. The Presidential Early Career Award for Scientists and Engineers (PECASE) embodies the high priority placed by the federal government on maintaining the leadership position of the United States in science by producing outstanding scientists and engineers and nurturing their continued . /Filter /FlateDecode His awards include the Presidential Early Career Award for Scientists and Engineers (2019), IJCAI Computers and Thought Award (2016), an NSF CAREER Award (2016), a Sloan Research Fellowship (2015), and a Microsoft Research Faculty Fellowship (2014). Percy Liang is an Associate Professor of Computer Science at Stanford University (B.S. 500 View details for DOI 10.1007/s10994-021-06119-y, View details for Web of Science ID 000722108900003, View details for Web of Science ID 000683104605062, View details for DOI 10.1145/3442381.3449992, View details for Web of Science ID 000733621803045, View details for Web of Science ID 000698679200153, View details for Web of Science ID 000683104606087, View details for Web of Science ID 000683104606074, View details for Web of Science ID 000683104602046, View details for Web of Science ID 000570978203005, View details for Web of Science ID 000683178505043, View details for Web of Science ID 000683178505055, View details for Web of Science ID 000683178505031, View details for Web of Science ID 000554408100007, View details for Web of Science ID 000570978202069, View details for Web of Science ID 000570978202034, View details for Web of Science ID 000525055503355. However, existing datasets are often cross-sectional with each individual observed only once, making it impossible to apply traditional time-series methods. Associate Professor of Computer Science, Stanford University - Cited by 38,800 - machine learning - natural language processing . Training Classifiers with Natural Language Explanations. from MIT, 2004; Ph.D. from UC Berkeley, 2011). PW Koh, S Sagawa, H Marklund, SM Xie, M Zhang, A Balsubramani, International Conference on Machine Learning, 5637-5664, Advances in neural information processing systems 30, E Choi, H He, M Iyyer, M Yatskar, W Yih, Y Choi, P Liang, L Zettlemoyer, Y Carmon, A Raghunathan, L Schmidt, JC Duchi, PS Liang, Advances in neural information processing systems 32, New articles related to this author's research, Squad: 100,000+ questions for machine comprehension of text, Understanding black-box predictions via influence functions, Know what you don't know: Unanswerable questions for SQuAD, Semantic parsing on freebase from question-answer pairs, Adversarial examples for evaluating reading comprehension systems, Prefix-tuning: Optimizing continuous prompts for generation, On the opportunities and risks of foundation models, Certified defenses against adversarial examples, Distributionally robust neural networks for group shifts: On the importance of regularization for worst-case generalization, Strategies for pre-training graph neural networks, Learning dependency-based compositional semantics, Dropout training as adaptive regularization, Wilds: A benchmark of in-the-wild distribution shifts, Certified defenses for data poisoning attacks, Unlabeled data improves adversarial robustness, Compositional semantic parsing on semi-structured tables, Delete, retrieve, generate: a simple approach to sentiment and style transfer. His two research goals are (i) to make machine learning more robust, fair, and interpretable; and (ii) to make computers easier to communicate with through natural language. Lan, F., Lee, A., Liang, P., Navarrete, E., Wang, L., Leng, H., Sanchez, V., Yen, M., Wang, Y., Nguyen, P., Sun, N., Abilez, O., Lewis, R., Yamaguchi, Y., Ashley, E., Bers, D., Robbins, R., Longaker, M., Wu, J. Identifiability and unmixing of latent parse trees. Functionally, we successfully tracked the survival of ZFN-edited human embryonic stem cells and their differentiated cardiomyocytes and endothelial cells in murine models, demonstrating the use of ZFN-edited cells for preclinical studies in regenerative medicine.Our study demonstrates a novel application of ZFN technology to the targeted genetic engineering of human pluripotent stem cells and their progeny for molecular imaging in vitro and in vivo. Our model represents each individual's features over time as a nonlinear function of a low-dimensional, linearly-evolving latent state. His two research goals are (i) to make machine learning more robust, fair, and interpretable; and (ii) to make computers easier to communicate with through natural language. My research interests lie at the intersection of Machine Learning and Statistics. Students need to learn and advance in an open-minded and supportive environment. Steinhardt, J., Liang, P., Lee, D. D., Sugiyama, M., Luxburg, U. V., Guyon, Garnett, R. Simpler Context-Dependent Logical Forms via Model Projections. Carmon, Y., Raghunathan, A., Schmidt, L., Liang, P., Duchi, J. C., Wallach, H., Larochelle, H., Beygelzimer, A., d'Alche-Buc, F., Fox, E., Garnett, R. Training Classifiers with Natural Language Explanations. Davis, J., Gu, A., Choromanski, K., Dao, T., Re, C., Finn, C., Liang, P., Meila, M., Zhang, T. Robust Encodings: A Framework for Combating Adversarial Typos, Jones, E., Jia, R., Raghunathan, A., Liang, P., Assoc Computat Linguist. How much of a hypertree can be captured by windmills? arXiv . Liu, B., Hu, W., Leskovec, J., Liang, P., Pande, V. Inferring Multidimensional Rates of Aging from Cross-Sectional Data. The worst form of professor. Chaganty, A., Liang, P., Erk, K., Smith, N. A. from MIT, 2004; Ph.D. from UC Berkeley, 2011). R Bommasani, DA Hudson, E Adeli, R Altman, S Arora, S von Arx, W Hu, B Liu, J Gomes, M Zitnik, P Liang, V Pande, J Leskovec, Computational Linguistics 39 (2), 389-446, Advances in neural information processing systems 26, Proceedings of the 52nd Annual Meeting of the Association for Computational. Wang, S. I., Ginn, S., Liang, P., Manning, C. D., Barzilay, R., Kan, M. Y. Here, we will discuss current efforts to create iPSC-dependent patient-specific disease models. Get Stanford HAI updates delivered directly to your inbox. Liang, P., Bouchard-Ct, A., Klein, D., Taskar, B. MI #~__ Q$.R$sg%f,a6GTLEQ!/B)EogEA?l kJ^- \?l{ P&d\EAt{6~/fJq2bFn6g0O"yD|TyED0Ok-\~[`|4P,w\A8vD$+)%@P4 0L ` ,\@2R 4f %PDF-1.4 The infinite PCFG using hierarchical Dirichlet processes. His manner doesn't seem professional and often is considered abusive. from MIT, 2004; Ph.D. from UC Berkeley, 2011). Koh, P., Nguyen, T., Tang, Y., Mussmann, S., Pierson, E., Kim, B., Liang, P., Daume, H., Singh, A. Make sure to do your case briefs since it is 30% of your grade, and he even explains the subject on the midterm, so you know what you have to study. He is very polite, knowledgable, such a job to listen. Not sure what you can learn given his confusing behavior. Edward Feigenbaum Chaganty, A., Mussmann, S., Liang, P., Gurevych, Miyao, Y. Sharan, V., Kakade, S., Liang, P., Valiant, G., Diakonikolas, Kempe, D., Henzinger, M. Uncertainty Sampling is Preconditioned Stochastic Gradient Descent on Zero-One Loss. Frostig, R., Wang, S., Liang, P., Manning, C. D., Ghahramani, Z., Welling, M., Cortes, C., Lawrence, N. D., Weinberger, K. Q. Although his lecture might be informative, I won't take his class again as his communication style is uncomfortable to me. stream Feature Noise Induces Loss Discrepancy Across Groups. Data Recombination for Neural Semantic Parsing. His research spans theoretical machine learning to practical natural language processing; topics include semantic parsing, question answering, machine translation, online learning, method of moments, approximate inference, My current research interests center around building a theory to understand and improve neural network models. Liu, E., Haghgoo, B., Chen, A. S., Raghunathan, A., Koh, P., Sagawa, S., Liang, P., Finn, C., Meila, M., Zhang, T. Catformer: Designing Stable Transformers via Sensitivity Analysis. Lots of homework Tough grader Amazing lectures Respected Zhang, Y., Liang, P., Chaudhuri, K., Sugiyama, M. On the Accuracy of Influence Functions for Measuring Group Effects. He is also a strong proponent of reproducibility through the creation of CodaLab Worksheets. Genome Editing of Human Embryonic Stem Cells and Induced Pluripotent Stem Cells With Zinc Finger Nucleases for Cellular Imaging. His research spans many topics in machine learning and natural language processing, including robustness, interpretability, semantics, and reasoning. Trading Structure for features by '' count includes citations to the following articles in Scholar Editing of human stem., I., Klein, D. Scaling up abstraction refinement via pruning ZFN-mediated. His class again as his communication style is uncomfortable to me patient-specific disease models & amp ; learning! This `` Cited by '' count includes citations to the following articles in Scholar datasets are cross-sectional. Students need to learn about meta-learning & amp ; few-shot learning trading Structure for features in open-minded... 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